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020 _a9783540447672
_9978-3-540-44767-2
024 7 _a10.1007/978-3-540-44767-2
_2doi
050 4 _aQC178
050 4 _aQC173.5-173.65
072 7 _aPHDV
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072 7 _aPHDV
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082 0 4 _a530.1
_223
245 1 0 _aData Analysis in Cosmology
_h[electronic resource] /
_cedited by Vicent J. Martinez, Enn Saar, Enrique Martinez Gonzales, Maria Jesus Pons-Borderia.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2009.
300 _aXII, 636 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Physics,
_x0075-8450 ;
_v665
505 0 _aUniversal Tools -- The Sea of Wavelets -- Fisher Matrices and All That: Experimental Design and Data Compression -- Data Compression, Classification and Parameter Estimation. Methods: Examples from Astronomy -- Statistics of Cosmic Background Radiation -- Cosmic Microwave Background Anisotropies: The Power Spectrum and Beyond -- Cosmic Microwave Background Polarization Analysis -- Diffuse Source Separation in CMB Observations -- Techniques for Compact Source Extraction in CMB Maps -- Determination of Cosmological Parameters from Cosmic Microwave Background Anisotropies -- Cosmic Microwave Background Data Analysis: From Time-Ordered Data to Angular Power Spectra -- Statistics of Large-Scale Structure -- The Large-Scale Structure in the Universe: From Power Laws to Acoustic Peaks -- The Cosmic Web: Geometric Analysis -- Power Spectrum Estimation. I. Basics -- Power Spectrum Estiamtion II. Linear Maximum Likelihood -- to Higher Order Spatial Statistics in Cosmology -- Phase Correlations and Topological Measures of Large-Scale Structure -- Multiscale Methods -- Gaussian Fields and Constrained Simulations of the Large-Scale Structure -- Weak Gravitational Lensing -- Mass Reconstruction from Lensing.
520 _aThe amount of cosmological data has dramatically increased in the past decades due to an unprecedented development of telescopes, detectors and satellites. Efficiently handling and analysing new data of the order of terabytes per day requires not only computer power to be processed but also the development of sophisticated algorithms and pipelines. Aiming at students and researchers the lecture notes in this volume explain in pedagogical manner the best techniques used to extract information from cosmological data, as well as reliable methods that should help us improve our view of the universe.
650 0 _aStatistics.
650 1 4 _aClassical and Quantum Gravitation, Relativity Theory.
_0http://scigraph.springernature.com/things/product-market-codes/P19070
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_0http://scigraph.springernature.com/things/product-market-codes/S17020
650 2 4 _aNumerical and Computational Physics, Simulation.
_0http://scigraph.springernature.com/things/product-market-codes/P19021
700 1 _aMartinez, Vicent J.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSaar, Enn.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGonzales, Enrique Martinez.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aPons-Borderia, Maria Jesus.
_eeditor.
_4edt
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540860624
776 0 8 _iPrinted edition:
_z9783642063053
776 0 8 _iPrinted edition:
_z9783540239727
830 0 _aLecture Notes in Physics,
_x0075-8450 ;
_v665
856 4 0 _uhttps://doi.org/10.1007/978-3-540-44767-2
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912 _aZDB-2-LNP
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